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DOI: https://doi.org/10.4491/eer.2021.541
Modelling dissolved oxygen and biochemical oxygen demand using data-driven techniques
Pali Sahu, Shreenivas N Londhe, and Preeti S Kulkarni
Civil Department, Vishwakarma Institute of Information Technology, Savitribai Phule Pune University, Pune 411048, India
Corresponding Author: Pali Sahu ,Tel: +917276413542, Fax: +917552529472, Email: palisahu18@gmail.com
Received: October 28, 2021;  Accepted: May 10, 2022.
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Precise quantification of Biochemical oxygen demand (BOD) and Dissolved oxygen (DO) are critically important for water quality assessment as well as for development of various management policies. To calculate BOD and DO for any water sample, standard technique Winkler-Azide method is used which is cumbersome and prone to measurement error. Therefore, there is a need to device alternate Data Driven Technique (DDT). In the present study, three different DDT: Artificial Neural Network (ANN), Multi Gene Genetic Programming (MG-GP) and M5 Model Tree (M5T) have been used for DO as well as BOD prediction for 3 separate stretches of Mula-Mutha River situated in Pune, India. Additionally, attempt has been made to predict BOD using modelled DO; which shows possibility of using modelled parameter in development of another model. Performance of the models was assessed through, root mean square error (RMSE); mean absolute relative error (MARE) and coefficient of correlation (R). Results based on 3 stations indicate that ANN and MGGP both outperformed with R above 0.85 and RMSE below 1 mg/L for 2 stations out of 3. MGGP and M5T can grasp the influence parameter which can be seen from the input frequency distribution in MGGP and coefficient of input parameters in M5T.
Keywords: Artificial neural networks | Biochemical oxygen demand | Modelling | Multi gene genetic programming | Model tree
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